The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Random data compressing techniques have been provided based on, among others, Huffman coding, arithmetic coding, run-length coding. In 1948, C. E. Shannon defined entropy suggesting the lower limit of effective symbol coding based on probability model, and the result close to the suggested lower limit can be attained by various encoding techniques such as Huffman coding and arithmetic coding that were subsequently proposed. Since these encoding techniques can obtain a higher encoding efficiency in the event that the probability is weighted on a smaller number of symbols by entropy theory, they are not capable of having higher encoding efficiency in case of encoding data of a uniform distribution.
As a solution to this problem, nonlinear B-transform (bubble-transform) techniques were suggested to increase the encoding efficiency on data having the probability model with a uniform distribution and probability unbiased to the smaller number of symbols. However, the suggested B-transform techniques merely mathematically induced the upper limit of data of the uniform distribution but failed to suggest a solution to effectively encode binary symbols (‘0’ and ‘1’) that result from the nonlinear B-transform and thus could not provide an effective solution to the encoding operations. Therefore, there is a practical need to develop a technique to effectively encode the binary symbols generated from the nonlinear B-transform.